Monitoring Service Delivery in the Financial Crisis

Markus Goldstein(Interpreted by Jishnu Das)

The findings, interpretations and conclusions expressed in this presentation are entirely those of Markus Goldstein. They do not necessarily represent the views of Jishnu Das and/or his family.

Predictions• Lots of predictions that financial crisis will hit HD

outcomes

• This is almost certainly correct for health (Baird, Friedman and Schady, 2007)

Predictions

• And probably true for education in low-income countries

Source: Ferriera and Schady (2008)

Sources of the hit: Households

• Lots of micro-evidence that shocks to households (especially when they are correlated) lead to declines

Sources of the hit: Public Spending

• Some evidence that public spending declines with macro-shocks (Peru)

• Source: Paxson and Schady (2005)

Sources of the hit: Public Spending

• But this can be compensated for by donor spending (Indonesia)

Two Unknown Unknowns

• Will drops in public spending affect outcomes? If so, how?

• Do we have monitoring tools to capture household responses with sufficient speed to engender policy responses?

Remainder of presentation

• How new M&E tools are changing the way we think of the relationship between public spending & outcomes

• How these can be used for policy responses to the crisis

• A Proposal for monitoring in the crisis

Spending ≠ outcomes: In X-sections

& time-series

Source: WDR 2004

Why?Evidence from new studies

New Evidence: Results from PETSWhat you get is not what was

sanctioned

Across CountriesPercent of school grants that actually reach schools

Percent GNI per capita (2000)

GNI per capita PPP (2000)

Ghana 1997/98 51 330 1880Kenya 2004 (secondary school

bursary funds)78 250 810

Madagascar 2002 88 2050 4610Peru 2001 (utilities) 70 / 97 670 2280PNG (2001/2002) 72 / 93 280 510Tanzania 2002-2003 62 270 1250Uganda 1991-1995/2001 <20 / 80Zambia 2001 (discretion/rule) 24 / 90 320 740Ye and Canagarajah (2002) for Ghana; Republic of Kenya (2005) for Kenya; Francken (2003) for Madagascar; Instituto Apoyo and World Bank (2002) for Peru; World (Bank 2004)

for PNG; MOF, Government of Tanzania (2005) for Tanzania; Reinikka and Svensson (2005) for Uganda; Das et al. (2002) for Zambia.

Percent GNI per capita (2000)

GNI per capita PPP (2000)

Ghana 1997/98 51 330 1880Kenya 2004 (secondary school

bursary funds)78 250 810

Madagascar 2002 88 2050 4610Peru 2001 (utilities) 70 / 97 670 2280PNG (2001/2002) 72 / 93 280 510Tanzania 2002-2003 62 270 1250Uganda 1991-1995/2001 <20 / 80Zambia 2001 (discretion/rule) 24 / 90 320 740Ye and Canagarajah (2002) for Ghana; Republic of Kenya (2005) for Kenya; Francken (2003) for Madagascar; Instituto Apoyo and World Bank (2002) for Peru; World (Bank 2004)

for PNG; MOF, Government of Tanzania (2005) for Tanzania; Reinikka and Svensson (2005) for Uganda; Das et al. (2002) for Zambia.

Indonesia 2000: Sources of school funding by grant receipt and public/private status

0

1000

2000

3000

4000

5000

6000

7000

8000

Public-Received

Grant

Public-NoGrant

Private-Received

Grant

Private-NoGrant

Grant Local Central

Primary schools

0

20000

40000

60000

80000

100000

120000

140000

Public-Received

Grant

Public-NoGrant

Private-Received

Grant

Private-NoGrant

Grant Local Central

Junior Secondary schoolsIn public schools, local government spending adjusted in response to grant

No adjustment in private schools

Substitution between grants and local government funding

New Evidence from QSDS: Public expenditures crowd-out

private expenditures

Zambia 2001: Effect of a 100 Kwacha increase in expected and unexpected school grants on household expenditures on education

-50

-45

-40

-35

-30

-25

-20

-15

-10

-5

0Expected Unexpected

Kw

acha

Household spending falls by about 45 for each additional 100 Kwacha spent on anticipated grants

Substitution between grants and household spending

Source: Jishnu Das, Stefan Dercon, James Habyarimana, Pramilla Krishnan (2004)

Evidence experimentally replicated in India, with identical substitution coefficients

New Evidence: Absenteeism Surveys

Absence rates among teachers and health workers

Note: Surveys were all fielded in 2002 or 2003. Sources: Chaudhury et al (2006) except for PNG, World Bank (2004) and Zambia, Das et al (2005).

0

10

20

30

40

50

Bangladesh Ecuador India Indonesia Papua NewGuinea

Peru Zambia Uganda

Primary schools Primary health facilities

PNG 2002: Depletion of the effective supply of teachers

Source: PESD 2002.

8572 68

100

0

20

40

60

80

100

Teachers onpayroll

Minus "ghost"teachers

Minus absentteachers

Minus schoolsclosed "for

lack ofteachers"

Results from QSDS:Effective supply of teachers

As a consequence: Evidence from Quality Studies

Spending ≠ outcomes

• In health unqualified private sector doctors in Delhi provide better care than qualified public sector doctors

• In education the cost per percentage correct in tests is 3 times higher in public compared to private schools in Pakistan

So…

• There is plenty of room for improvements• BUT• There are good reasons to believe that we

don’t know enough– Can’t find any studies of the effects of

spending cuts that are causal (usually strongly correlated with economy wide problems)

Proposal based on lessons learnt

• Sentinel Surveillance Sites for Service Delivery (S4D)

IDEA

• High Frequency Surveys in limited sites (villages/neighborhoods)

• Collect information using a variety of different tools

• Household Surveys• Facility surveys (education health)• Outcomes (for instance, learning)

Precursors• ICRISAT• More recently

– Financial Diaries in Bangladesh, India and South Africa (Bi-weekly for one year)

– Morbidity and Health Seeking behavior in Delhi, India (Weekly and Monthly for 2 years)

– LEAPS in Pakistan (Annually for 4 years)

• All these surveys are yielding valuable (and new) information on household and provider behavior—particularly in relation to shocks

The S4D Outline

• Work with limited sampling sites in a number of countries at higher frequency than usual

• Collect periodic information from– Households (Main outcomes, labor, wages)– Facilities (Absenteeism, PETS, Quality)– Higher administrative units to track budgets

(Funds availability and flows)• Fixed Reporting Formats + Public Data

Summary

• Financial Crisis will potentially lead to worse HD outcomes

• But• This is also an opportunity

– To address inefficiencies in the delivery of services

– To set up a M framework that can provide real-time information for real-time policy

• Deepen our understanding of HD processes

Additional slides for discussion

Ethiopia

MozambiqueMalaw i

Eritrea

Burundi

Tanzania

Sierra Leone

Nepal

Niger

Burkina Faso ChadGuinea-Bissau

Rw anda

Sudan

Madagascar

Nigeria

Mali

Cambodia

Vietnam

Yemen, Rep.

Uganda

Bangladesh

Central African Republic

Togo

KenyaGambia, The

Benin

Lao PDR GhanaIndia

Haiti

Mongolia

ZambiaMauritania

Pakistan

Uzbekistan

Lesotho

Angola

China

Senegal

Guinea

Azerbaijan

Tajikistan

Zimbabw e

Cameroon

Sri Lanka

Honduras

Albania

Cote d'Ivoire

GeorgiaSyrian Arab Republic

Bolivia

ArmeniaMoldova

Indonesia

Papua New GuineaCongo, Rep.

Kyrgyz Republic

Egypt, Arab Rep.Philippines

Ukraine

Morocco

Guatemala

Iran, Islamic Rep.

Bulgaria

West Bank and Gaza

Kazakhstan

Romania

Ecuador

Algeria

Jordan

Dominican Republic

El Salvador

Turkmenistan

Paraguay

Jamaica

Tunisia

Lithuania

Peru

Namibia

Colombia

Latvia

Macedonia, FYR

Belarus

ThailandLebanon

Russian Federation

Turkey

Panama

Poland

Mexico

Costa Rica

Venezuela, RB

Mauritius

Slovak Republic

Estonia

South Africa

Malaysia

Brazil

Trinidad and Tobago

Croatia

Chile

Hungary

Czech Republic

Oman

Uruguay

Saudi Arabia

Argentina

SloveniaKorea, Rep.

PortugalGreeceSpain

Kuw ait

Israel New ZealandIreland ItalyUnited KingdomCanadaAustraliaFinland FranceNetherlands

Belgium

Sw eden

United States

Austria Germany

Norw ay

DenmarkJapan Sw itzerland

-150

-100

-50

0

50

100

150

-150 -100 -50 0 50 100 150

Per capita public spending on health 1990s average (Log difference between actual and predicted by GDP per capita x100)

Und

er-5

mor

talty

rate

200

0(L

og d

iffer

ence

bet

wee

n ac

tual

and

pre

dict

ed b

y G

DP

per

cap

ita x

100)

And the same for health…

…and vastly different changes in spending can be associated with similar changes in outcomes.

Figure 1: Official vs. Effective Expenditures by Regional Health Delegations

• CHAD: On average, RHDs received only 26,7% of their official non-wage budgetary expenditures from the MoH

0

100

200

300

400

500

600

700

800

900

1000

Mill

ions

of C

FA F

ranc

s

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5

10

15

20

25

30

35

40

45

Perc

enta

ge R

ecei

ved

Initial Allocation

Resources Received

Percentage

Average

Percent of time present, and percent of time teaching

Beyond absenteeism: Effective supply of teaching

0

20

40

60

80

100

Tunisia Egypt Morocco Yemen Pernambuco(Brazil)

Lebanon Ghana

Potential time Presence time Time on task

Sources: Lane and Millot (2002); Abhadzi, Millot and Prouty (2006).

Private MBBS Public MBBS Private, No MBBS

Health: What they know, what they do40% of essential questions asked

Perc

enta

ge o

f Ess

entia

l Tas

ks C

ompl

eted

Source: Das and Hammer (2008)

Education: Its not the money

260

184

259

75 6882

0

50

100

150

200

250

Cos

t for

Eve

ry P

erce

nt C

orre

ct (i

n R

s.)

Government Private

Cost of Schooling

English Math Urdu

Source: Pakistan, The LEAPS Report